Research trends and hot spots in the prevention and management of radiation dermatitis: a bibliometric analysis based on CiteSpace
Bibliographic record
Abstract
OBJECTIVE: This study sought to examine the current state and explore the key areas and emerging trends in radiation dermatitis prevention and management through bibliometric analysis, with the goal of providing valuable insights for future research endeavors. METHODS: This study analyzed all publications on radiation dermatitis prevention and management from the Web of Science (WOS) core database up to 2024. The CiteSpace software was utilized to visualize authors, countries/regions, publishing institutions, keywords, co-cited documents, hot spots, and research frontiers. RESULTS: A total of 459 articles (1995-2024) were identified, with the overall number of publications demonstrating an increasing trend. The United States (125) produced the highest number of publications, followed by China (73) and Canada (45). Key research topics encompass breast cancer, head and neck cancer, acute radiation dermatitis, and radiation recall dermatitis. Double-blind clinical trials constitute the primary research methodology. The main research areas in this field focus on the role of radiotherapy dose fractionation modalities, atmospheric pressure cold plasma, hyperbaric oxygen therapy (HBOT), aloe vera, biomodulation therapy, and biological dressings in the prevention and management of radiation dermatitis. CONCLUSION: This comprehensive bibliometric analysis reveals that risk prediction, assessment tools, and the efficacy of radiodermatitis are prominent research topics in the field. These areas are currently experiencing rapid growth and warrant further attention from researchers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.141 | 0.156 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".